@article{10.11648/j.her.20251003.12,
  author = {Loide Stefanus and Gabriel Tuhafeni Nhinda and Irja Naambo Shaanika},
  title = {Teachers' AI Adoption in Namibia: A Theory of Planned Behaviour Analysis
},
  journal = {Higher Education Research},
  volume = {10},
  number = {3},
  pages = {77-87},
  doi = {10.11648/j.her.20251003.12},
  url = {https://doi.org/10.11648/j.her.20251003.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.her.20251003.12},
  abstract = {Artificial Intelligence (AI) holds transformative potential for education, yet it remains excluded from Namibia’s secondary school curriculum. The reasons for this exclusion are unclear. However, the COVID-19 pandemic exposed fundamental flaws in the lack of AI integration, as the education sector—like many others—faced significant disruptions. Introducing AI education and incorporating its use in secondary schools could enable Namibia to leapfrog in innovation and harness the opportunities presented by emerging technologies. This could drive both innovation and socio-economic growth. For this to occur, it is essential to understand teachers’ perspectives on teaching AI. Grounded in the Theory of Planned Behaviour (TPB), this study investigates Namibian teachers’ behavioural intentions to teach AI, examining the roles of attitudes, subjective norms, and perceived behavioural control, alongside implementation challenges. A mixed-methods approach was adopted, combining surveys with exploratory factor analysis (EFA) involving 22 teachers from the Khomas Region. While the primary aim—assessing TPB constructs (attitudes, subjective norms, and intentions)—was achieved, predictive analysis was limited by sample size. EFA extracted three TPB-aligned factors: attitude (ATT), behavioural intention (BI), and subjective norm (SN). However, statistical power was insufficient for regression or structural equation modelling (KMO = 0.50; Bartlett’s p < 0.001), reflecting the need for broader sampling. Demographically, most participants (54.5% male, 45.5% female) were aged 30–39, held honours degrees, and taught in urban public schools. Despite low perceived behavioural control (e.g., limited resources), teachers reported strong intentions to teach AI, driven by positive attitudes and social expectations. The findings highlight the TPB’s relevance in Namibia’s AI education context while revealing systemic barriers. To facilitate adoption, policymakers must address resource gaps, provide teacher training, and improve infrastructure. This study offers a foundational TPB-based framework for future research in under-resourced educational settings.
},
 year = {2025}
}

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TY  - JOUR
T1  - Teachers' AI Adoption in Namibia: A Theory of Planned Behaviour Analysis

AU  - Loide Stefanus
AU  - Gabriel Tuhafeni Nhinda
AU  - Irja Naambo Shaanika
Y1  - 2025/05/22
PY  - 2025
N1  - https://doi.org/10.11648/j.her.20251003.12
DO  - 10.11648/j.her.20251003.12
T2  - Higher Education Research
JF  - Higher Education Research
JO  - Higher Education Research
SP  - 77
EP  - 87
PB  - Science Publishing Group
SN  - 2578-935X
UR  - https://doi.org/10.11648/j.her.20251003.12
AB  - Artificial Intelligence (AI) holds transformative potential for education, yet it remains excluded from Namibia’s secondary school curriculum. The reasons for this exclusion are unclear. However, the COVID-19 pandemic exposed fundamental flaws in the lack of AI integration, as the education sector—like many others—faced significant disruptions. Introducing AI education and incorporating its use in secondary schools could enable Namibia to leapfrog in innovation and harness the opportunities presented by emerging technologies. This could drive both innovation and socio-economic growth. For this to occur, it is essential to understand teachers’ perspectives on teaching AI. Grounded in the Theory of Planned Behaviour (TPB), this study investigates Namibian teachers’ behavioural intentions to teach AI, examining the roles of attitudes, subjective norms, and perceived behavioural control, alongside implementation challenges. A mixed-methods approach was adopted, combining surveys with exploratory factor analysis (EFA) involving 22 teachers from the Khomas Region. While the primary aim—assessing TPB constructs (attitudes, subjective norms, and intentions)—was achieved, predictive analysis was limited by sample size. EFA extracted three TPB-aligned factors: attitude (ATT), behavioural intention (BI), and subjective norm (SN). However, statistical power was insufficient for regression or structural equation modelling (KMO = 0.50; Bartlett’s p < 0.001), reflecting the need for broader sampling. Demographically, most participants (54.5% male, 45.5% female) were aged 30–39, held honours degrees, and taught in urban public schools. Despite low perceived behavioural control (e.g., limited resources), teachers reported strong intentions to teach AI, driven by positive attitudes and social expectations. The findings highlight the TPB’s relevance in Namibia’s AI education context while revealing systemic barriers. To facilitate adoption, policymakers must address resource gaps, provide teacher training, and improve infrastructure. This study offers a foundational TPB-based framework for future research in under-resourced educational settings.

VL  - 10
IS  - 3
ER  - 

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